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Automatic Radial Distortion Estimation from a Single Image

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Abstract

Many computer vision algorithms rely on the assumptions of the pinhole camera model, but lens distortion with off-the-shelf cameras is usually significant enough to violate this assumption. Many methods for radial distortion estimation have been proposed, but they all have limitations. Robust automatic radial distortion estimation from a single natural image would be extremely useful for many applications, particularly those in human-made environments containing abundant lines. For example, it could be used in place of an extensive calibration procedure to get a mobile robot or quadrotor experiment up and running quickly in an indoor environment. We propose a new method for automatic radial distortion estimation based on the plumb-line approach. The method works from a single image and does not require a special calibration pattern. It is based on Fitzgibbon’s division model, robust estimation of circular arcs, and robust estimation of distortion parameters. We perform an extensive empirical study of the method on synthetic images. We include a comparative statistical analysis of how different circle fitting methods contribute to accurate distortion parameter estimation. We finally provide qualitative results on a wide variety of challenging real images. The experiments demonstrate the method’s ability to accurately identify distortion parameters and remove distortion from images.

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  1. See http://www.cs.ait.ac.th/vgl/faisal/downloads.html.

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Acknowledgements

Faisal Bukhari was supported by graduate fellowships from the Higher Education Commission of Pakistan and the Asian Institute of Technology (AIT), Thailand. We are grateful to Irshad Ali and Waheed Iqbal for comments on this work.

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Authors and Affiliations

  1. School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani, 12120, Thailand

    Faisal Bukhari & Matthew N. Dailey

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  1. Faisal Bukhari
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Correspondence to Faisal Bukhari.

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The authors are with the Computer Science and Information Management program.

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Bukhari, F., Dailey, M.N. Automatic Radial Distortion Estimation from a Single Image. J Math Imaging Vis 45, 31–45 (2013). https://doi.org/10.1007/s10851-012-0342-2

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